776 research outputs found
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of
graph-based machine learning methods. We release a new data set, compiled from
International Planning Competitions (IPC), for benchmarking graph
classification, regression, and related tasks. Apart from the graph
construction (based on AI planning problems) that is interesting in its own
right, the data set possesses distinctly different characteristics from
popularly used benchmarks. The data set, named IPC, consists of two
self-contained versions, grounded and lifted, both including graphs of large
and skewedly distributed sizes, posing substantial challenges for the
computation of graph models such as graph kernels and graph neural networks.
The graphs in this data set are directed and the lifted version is acyclic,
offering the opportunity of benchmarking specialized models for directed
(acyclic) structures. Moreover, the graph generator and the labeling are
computer programmed; thus, the data set may be extended easily if a larger
scale is desired. The data set is accessible from
\url{https://github.com/IBM/IPC-graph-data}.Comment: ICML 2019 Workshop on Learning and Reasoning with Graph-Structured
Data. The data set is accessible from https://github.com/IBM/IPC-graph-dat
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI. Since no single
planner can work well for all tasks and domains, portfolio-based techniques
have become increasingly popular in recent years. In particular, deep learning
emerges as a promising methodology for online planner selection. Owing to the
recent development of structural graph representations of planning tasks, we
propose a graph neural network (GNN) approach to selecting candidate planners.
GNNs are advantageous over a straightforward alternative, the convolutional
neural networks, in that they are invariant to node permutations and that they
incorporate node labels for better inference.
Additionally, for cost-optimal planning, we propose a two-stage adaptive
scheduling method to further improve the likelihood that a given task is solved
in time. The scheduler may switch at halftime to a different planner,
conditioned on the observed performance of the first one. Experimental results
validate the effectiveness of the proposed method against strong baselines,
both deep learning and non-deep learning based.
The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at
https://github.com/matenure/GNN_planner. Data set is released at
https://github.com/IBM/IPC-graph-dat
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Hierarchical abstractions are a methodology for solving large-scale graph
problems in various disciplines. Coarsening is one such approach: it generates
a pyramid of graphs whereby the one in the next level is a structural summary
of the prior one. With a long history in scientific computing, many coarsening
strategies were developed based on mathematically driven heuristics. Recently,
resurgent interests exist in deep learning to design hierarchical methods
learnable through differentiable parameterization. These approaches are paired
with downstream tasks for supervised learning. In practice, however, supervised
signals (e.g., labels) are scarce and are often laborious to obtain. In this
work, we propose an unsupervised approach, coined OTCoarsening, with the use of
optimal transport. Both the coarsening matrix and the transport cost matrix are
parameterized, so that an optimal coarsening strategy can be learned and
tailored for a given set of graphs. We demonstrate that the proposed approach
produces meaningful coarse graphs and yields competitive performance compared
with supervised methods for graph classification and regression.Comment: AAAI 2021. Code is available at
https://github.com/matenure/OTCoarsenin
Rational design of a polyoxometalate intercalated layered double hydroxide: highly efficient catalytic epoxidation of allylic alcohols under mild and solvent-free conditions
Intercalation catalysts, owing to their modular and accessible gallery and unique interlamellar chemical environment, have shown wide application in various catalytic reactions. However, the poor mass transfer between the active components of the intercalated catalysts and organic substrates is one of the challenges that limit their further application. Herein, we have developed a novel heterogeneous catalyst by intercalating the polyoxometalate (POM) of Na9LaW10O36⋅32 H2O (LaW10) into layered double hydroxides (LDHs), which have been covalently modified with ionic liquids (ILs). The intercalation catalyst demonstrates high activity and selectivity for the epoxidation of various allylic alcohols in the presence of H2O2. For example, trans-2-hexen-1-ol undergoes up to 96 % conversion and 99 % epoxide selectivity at 25 °C in 2.5 h. To the best of our knowledge, the Mg3Al−ILs−C8−LaW10 composite material constitutes one of the most efficient heterogeneous catalysts for the epoxidation of allylic alcohols (including the hydrophobic allylic alcohols with long alkyl chains) reported so far
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
Learning an effective global model on private and decentralized datasets has
become an increasingly important challenge of machine learning when applied in
practice. Existing distributed learning paradigms, such as Federated Learning,
enable this via model aggregation which enforces a strong form of modeling
homogeneity and synchronicity across clients. This is however not suitable to
many practical scenarios. For example, in distributed sensing, heterogeneous
sensors reading data from different views of the same phenomenon would need to
use different models for different data modalities. Local learning therefore
happens in isolation but inference requires merging the local models to achieve
consensus. To enable consensus among local models, we propose a feature fusion
approach that extracts local representations from local models and incorporates
them into a global representation that improves the prediction performance.
Achieving this requires addressing two non-trivial problems. First, we need to
learn an alignment between similar feature components which are arbitrarily
arranged across clients to enable representation aggregation. Second, we need
to learn a consensus graph that captures the high-order interactions between
local feature spaces and how to combine them to achieve a better prediction.
This paper presents solutions to these problems and demonstrates them in
real-world applications on time series data such as power grids and traffic
networks
Construction of a Fish-like Robot Based on High Performance Graphene/PVDF Bimorph Actuation Materials.
Smart actuators have many potential applications in various areas, so the development of novel actuation materials, with facile fabricating methods and excellent performances, are still urgent needs. In this work, a novel electromechanical bimorph actuator constituted by a graphene layer and a PVDF layer, is fabricated through a simple yet versatile solution approach. The bimorph actuator can deflect toward the graphene side under electrical stimulus, due to the differences in coefficient of thermal expansion between the two layers and the converse piezoelectric effect and electrostrictive property of the PVDF layer. Under low voltage stimulus, the actuator (length: 20 mm, width: 3 mm) can generate large actuation motion with a maximum deflection of about 14.0 mm within 0.262 s and produce high actuation stress (more than 312.7 MPa/g). The bimorph actuator also can display reversible swing behavior with long cycle life under high frequencies. on this basis, a fish-like robot that can swim at the speed of 5.02 mm/s is designed and demonstrated. The designed graphene-PVDF bimorph actuator exhibits the overall novel performance compared with many other electromechanical avtuators, and may contribute to the practical actuation applications of graphene-based materials at a macro scale
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